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Graphical Models for Processing Missing Data

arXiv 1801.03583 ↗First posted 10 Jan 2018stat.MEcited by 57

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Abstract (arXiv)

This paper reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three different perspectives: textittransparency, estimability and testability. We then show how procedures based on graphical models can overcome these limitations and provide meaningful performance guarantees even when data are Missing Not At Random (MNAR). In particular, we identify conditions that guarantee consistent estimation in broad categories of missing data problems, and derive procedures for implementing this estimation. Finally we derive testable implications for missing data models in both MAR (Missing At Random) and MNAR categories.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jan 2018.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 10; 2024: 10; 2025: 11; 2026: 10.

57 papers.

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  1. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 21
  2. 2023Causal Deep Learningcited by 20
  3. 2021Deconfounded Causal Collaborative Filteringcited by 14
  4. 2021Greedy structure learning from data that contain systematic missing valuescited by 12
  5. 2022Domain Adaptation under Missingness Shiftcited by 11
  6. 2021Obtaining Causal Information by Merging Datasets with MAXENTcited by 9
  7. 2022To Impute or not to Impute? Missing Data in Treatment Effect Estimationcited by 9
  8. 2021Causal Learning for Socially Responsible AIcited by 7
  9. 2022Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairnesscited by 6
  10. 2024Optimal Transport for Structure Learning Under Missing Datacited by 5
  11. 2024Causality from Bottom to Top: A Surveycited by 5
  12. 2024A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Modelscited by 5
38 more
  1. 2024Explainability of Machine Learning Models under Missing Datacited by 5
  2. 2022On Testability and Goodness of Fit Tests in Missing Data Modelscited by 4
  3. 2025CausalMan: A physics-based simulator for large-scale causalitycited by 4
  4. 2023The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Studycited by 3
  5. 2023Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lenscited by 3
  6. 2023Identification of Causal Structure in the Presence of Missing Data with Additive Noise Modelcited by 3
  7. 2021Multiple imputation and test-wise deletion for causal discovery with incomplete cohort datacited by 2
  8. 2022Missing Data Infill with Automungecited by 2
  9. 2022Principal Component Analysis based frameworks for efficient missing data imputation algorithmscited by 2
  10. 2023Conditional expectation with regularization for missing data imputationcited by 2
  11. 2023Correcting for Selection Bias and Missing Response in Regression using Privileged Informationcited by 2
  12. 2023Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reductioncited by 2
  13. 2023A Complete Characterisation of Structured Missingnesscited by 2
  14. 2024Cross-validating causal discovery via Leave-One-Variable-Outcited by 2
  15. 2022Parsed Categoric Encodings with Automungecited by 1
  16. 2022I Prefer not to Say: Protecting User Consent in Models with Optional Personal Datacited by 1
  17. 2024Robust prediction under missingness shiftscited by 1
  18. 2024MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Datacited by 1
  19. 2025ICYM2I: The illusion of multimodal informativeness under missingnesscited by 1
  20. 2025Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Supportcited by 1
  21. 2025FLOSS: Federated Learning with Opt-Out and Straggler Supportcited by 1
  22. 2025Linear Regression under Missing or Corrupted Coordinatescited by 1
  23. 2026Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solutioncited by 1
  24. 2026Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputationcited by 1
  25. 2021Prediction in the presence of response-dependent missing labelscited by 0
  26. 2022Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarizationcited by 0
  27. 2023Towards a Transportable Causal Network Model Based on Observational Healthcare Datacited by 0
  28. 2023Imputation using training labels and classification via label imputationcited by 0
  29. 2024Directly Handling Missing Data in Linear Discriminant Analysis for Enhancing Classification Accuracy and Interpretabilitycited by 0
  30. 2024Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confoundingcited by 0
  31. 2024Multi-armed Bandits with Missing Outcomecited by 0
  32. 2025Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confoundingcited by 0
  33. 2025Linear-Time Primitives for Algorithm Development in Graphical Causal Inferencecited by 0
  34. 2025Causal identification with Y0cited by 0
  35. 2025Markov Missing Graph: A Graphical Approach for Missing Data Imputationcited by 0
  36. 2025Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"cited by 0
  37. 2025Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivitycited by 0
  38. 2026Drop the mask! GAMM-A Taxonomy for Graph Attributes Missing Mechanismscited by 0

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